Integrating Data, Resources, and Capabilities in the Great Lakes: Impacting Federal, State, Tribal, Local, and Private Partnerships through the Area Committee
Bibliographic record
Abstract
ABSTRACT The Great Lakes of the United States and Canada comprise, essentially, a closed freshwater system in which over 31 million individuals coexist within the industrial, agricultural, and recreational economies of the region. With annual replacement rates of less than one percent, pollutants introduced into the Great Lakes can, over time, concentrate, resuspend, and infiltrate the biologic system through annual lake turnover, navigational dredging, and cycling through the food web. Past spills of oil and hazardous substances resulted in highly elevated levels of byproducts such as polycyclic aromatic hydrocarbons (PAHs), heavy metals like mercury, and toxics including polychlorinated biphenyls (PCBs). These have resulted in stream degradation, fish consumption advisories, fish kills, and beach closures, with impacts felt at the local, watershed, regional, and binational levels. Traditionally, in the Great Lakes, local and regional policy groups have interacted with the Federal government through such programs as Areas of Concern (AOC) and the LakeWide Management Plan (LaMP) process; but these venues have not allowed direct interaction with the response and remedial community at any level. Area Committees, on the other hand, and also operating at more or less the local level and with a broad spectrum of participation, are uniquely situated to evolve and assist with specific issues on a timely basis. The coordinative and collaborative abilities of the Area Committee allow for initiating timely information gathering, focused decision-making, and action implementation, if necessary. The benefits of Area Committee involvement are two fold and profit both the policy and response groups in the Great Lakes.The concerns, historic practices, and larger-scale/longer-term vision can be vetted through the policy groups to the Area Committee.The Area Committee can, in turn, influence its members in specific directions through modified scopes of investigation and training/exercising that specifically address Area responsibilities. The Area Committee's ability to expand its audience and augment its knowledge base is critical to developing a fully integrated multi-tiered and multi-missioned planning, preparedness, and response community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".